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# AI/ML Engineer - Generative AI & Banking

- **Company:** Generative Ai
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $118,560.0
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Frameworks, Microsoft Azure, Big Data, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Graph Database, Python (Programming Language), Machine Learning, Natural Language Processing, NLTK (NLP Analysis), Performance Tuning, Tensorflow, Azure Machine Learning, SQL Databases, Data Streaming, Enterprise Software Applications, Data Ingestion, Pytorch, LangChain, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Spring-boot, Deep Learning, Generative AI, Microsoft Fabric, Data Lakes, AngularJS, Scikit Learn, Kubernetes, HuggingFace, Machine Learning Operations, Virtual Agents, Spacy, Semantic Kernel, Artificial Intelligence Markup Language (AIML), Docker, AI Co-pilot, Databricks, Microservices
- **Published:** October 21, 2026
- **Apply:** https://www.dice.com/job-detail/6592cc55-1ee7-4044-97bb-c44d7b1c2eac

## About the Role

* AI ML Engineer Generative AI & Banking
 * Java Springboot
 * Angular
 * AI orchestration
 * MLOps

## Description

Design and develop AI/ML solutions using supervised, unsupervised, deep learning, NLP, time series forecasting, and anomaly detection techniques to address business challenges.

 2. Build Generative AI applications leveraging LLMs, prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), and AI agent frameworks.

 1. Experience of working for banking domain

 2. Develop and maintain end-to-end AI pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.

 3. Demonstrate strong programming expertise in Python and SQL, with hands-on experience in ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and NLTK.

 4. Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric., Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.

 6. Deploy AI models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.

 7. Utilize cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.

 8. Collaborate with business and technology stakeholders to translate business requirements into scalable AI solutions while tracking ROI and value realization.

 9. Contribute to AI innovation and best practices through reusable frameworks, AI copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques

Nice to Have Skills: Java Springboot, Angular